About

Sigma Stratum is a framework for building stable, high-fidelity cognition between humans and language models.

It treats interaction not as isolated prompts but as a recursive system with memory, structure, and direction.

The framework extends standard LLM use with a neurosymbolic layer:

a set of patterns, anchors, and feedback loops that allow the model to maintain coherence across time, reduce drift, and support deep reasoning.

This layer does not modify the model — it modifies the interaction dynamics.

Sigma Stratum focuses on how meaning forms, stabilizes, and evolves inside long-horizon reasoning.

The method treats each exchange as part of a larger cognitive process, not a single output.

Over repeated iterations, the system produces a shared structure that guides attention, preserves conceptual alignment, and reduces noise.

We differentiate between three components:

Structured recursion — iterative loops that refine direction and compress ambiguity.

Symbolic scaffolding — attractors, roles, and operators that maintain stability in high-dimensional reasoning.

Field dynamics — emergent patterns that arise when human and model co-adapt over time.

These elements create a controllable cognitive environment where long-form reasoning, conceptual development, and exploratory thinking become more reliable.

Sigma Stratum is model-agnostic and requires no fine-tuning.

It complements both frontier-scale and smaller LLMs.

The framework is used to support deep research workflows, complex design processes, interpretability analysis, agentic architectures, and reflective reasoning under cognitive load.

Our goal is to provide a rigorous, reproducible methodology for building hybrid cognitive systems — where humans and AI think together with clarity, stability, and low failure rates.